Error in "predict" in the "Gesture Recognition using Videos and Deep Learning" example

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Hello
I am trying to run the example of "Gesture Recognition using Videos and Deep Learning" in Matlab.
However, when the code reaches to the line below
dlYPred = predict(slowFastClassifier,dlVideo);
Matlab returns the error below:
Error using predict
No valid system or dataset was specified.
Does anyone have any sugesttion how to resolve the issue?
Thanks
  4 Comments
SM
SM on 19 Sep 2022
Thanks for your resposne.
Have you used ".mlx" file to run it or you created a ".m" file and reproduced the example?
Becauase, when I used the live script (.mlx) it runs with no problem. However, when I copy the codes into a ".m" file, and try to reprocude the same, results, I face such error. I have checket the syntax, and functions to be correct.
I cannot figure out what mistake I made?
Here is the code that I try to run:
I downloaded the files and put then in the path, and all functions are in the code directory.
clear
clc
%%% Expample Link: https://au.mathworks.com/help/vision/ug/gesture-recognition-using-slowfast-video-classification.html
%{
For this code the slowFastVideoClassifier add-on must be installed
%}
%%
downloadFolder='./Gesture'
%% Getting the classes
pretrainedDataFile =strcat(downloadFolder,'/slowFastPretrained_fourClasses.mat');
pretrained = load(pretrainedDataFile);
slowFastClassifier = pretrained.data.Params;
classes = slowFastClassifier.Classes
%%
doTraining = false;
groundTruthFolder = fullfile(downloadFolder,"groundTruthFolder");
if ~isfolder(groundTruthFolder)
mkdir(groundTruthFolder);
end
groundTruthFolder = fullfile(downloadFolder,"groundTruthFolder");
trainingFolder = fullfile(downloadFolder,"videoScenes");
extractVideoScenes(groundTruthFolder,trainingFolder,classes);
%%
numFrames = 16;
frameSize = [112,112];
numChannels = 3;
isDataForTraining = true;
dsTrain = createFileDatastore(trainingFolder,numFrames,numChannels,classes,isDataForTraining);
baseNetwork = "resnet50-3d";
inputSize = [frameSize,numChannels,numFrames];
slowFast = slowFastVideoClassifier(baseNetwork,string(classes),InputSize=inputSize);
slowFast.ModelName = "Gesture Recognizer Using Deep Learning";
dsTrain = transform(dsTrain,@augmentVideo);
preprocessInfo.Statistics = slowFast.InputNormalizationStatistics;
preprocessInfo.InputSize = inputSize;
preprocessInfo.SizingOption = "resize";
dsTrain = transform(dsTrain,@(data)preprocessVideoClips(data,preprocessInfo));
params.Classes = classes;
params.MiniBatchSize = 5;
params.NumIterations = 600;
params.CosineNumIterations = [100 200 300];
params.SaveBestAfterIteration = 400;
params.MinLearningRate = 1e-4;
params.MaxLearningRate = 1e-3;
params.Momentum = 0.9;
params.Velocity = [];
params.L2Regularization = 0.0005;
params.ProgressPlot = false;
params.Verbose = true;
params.DispatchInBackground = true;
params.NumWorkers = 12;
%% Train Video Classifieor
params.ModelFilename = "slowFastPretrained_fourClasses.mat";
if doTraining
epoch = 1;
bestLoss = realmax;
accTrain = [];
lossTrain = [];
iteration = 1;
start = tic;
trainTime = start;
shuffled = shuffleTrainDs(dsTrain);
% Number of outputs is two: One for RGB frames, and one for ground truth labels.
numOutputs = 2;
mbq = createMiniBatchQueue(shuffled, numOutputs, params);
% Use the initializeTrainingProgressPlot and initializeVerboseOutput
% supporting functions, listed at the end of the example, to initialize
% the training progress plot and verbose output to display the training
% loss, training accuracy, and validation accuracy.
plotters = initializeTrainingProgressPlot(params);
initializeVerboseOutput(params);
while iteration <= params.NumIterations
% Iterate through the data set.
[dlX1,dlY] = next(mbq);
% Evaluate the model gradients and loss using dlfeval.
[gradients,loss,acc,state] = ...
dlfeval(@modelGradients,slowFast,dlX1,dlY);
% Accumulate the loss and accuracies.
lossTrain = [lossTrain, loss];
accTrain = [accTrain, acc];
% Update the network state.
slowFast.State = state;
% Update the gradients and parameters for the video classifier
% using the SGDM optimizer.
[slowFast,params.Velocity,learnRate] = ...
updateLearnables(slowFast,gradients,params,params.Velocity,iteration);
if ~hasdata(mbq) || iteration == params.NumIterations
% Current epoch is complete. Do validation and update progress.
trainTime = toc(trainTime);
accTrain = mean(accTrain);
lossTrain = mean(lossTrain);
% Update the training progress.
displayVerboseOutputEveryEpoch(params,start,learnRate,epoch,iteration,...
accTrain,lossTrain,trainTime);
updateProgressPlot(params,plotters,epoch,iteration,start,lossTrain,accTrain);
% Save the trained video classifier and the parameters, that gave
% the best training loss so far. Use the saveData supporting function,
% listed at the end of this example.
bestLoss = saveData(slowFast,bestLoss,iteration,lossTrain,params);
end
if ~hasdata(mbq) && iteration < params.NumIterations
% Current epoch is complete. Initialize the training loss, accuracy
% values, and minibatchqueue for the next epoch.
accTrain = [];
lossTrain = [];
epoch = epoch + 1;
trainTime = tic;
shuffled = shuffleTrainDs(dsTrain);
mbq = createMiniBatchQueue(shuffled, numOutputs, params);
end
iteration = iteration + 1;
end
% Display a message when training is complete.
endVerboseOutput(params);
disp("Model saved to: " + params.ModelFilename);
end
%% Evaluate the trained data
isDataForTraining = false;
dsEval = createFileDatastore(trainingFolder,numFrames,numChannels,classes,isDataForTraining);
dsEval = transform(dsEval,@(data)preprocessVideoClips(data,preprocessInfo));
if doTraining
transferLearned = load(params.ModelFilename);
slowFastClassifier = transferLearned.data.slowFast;
end
numOutputs = 2;
mbq = createMiniBatchQueue(dsEval,numOutputs,params);
numClasses = numel(params.Classes);
cmat = sparse(numClasses,numClasses);
while hasdata(mbq)
[dlVideo,dlY] = next(mbq);
% Computer the predictions of the trained SlowFast
% video classifier.
dlYPred = predict(slowFastClassifier,dlVideo);
dlYPred = squeezeIfNeeded(dlYPred,dlY);
% Aggregate the confusion matrix by using the maximum
% values of the prediction scores and the ground truth labels.
[~,YTest] = max(dlY,[],1);
[~,YPred] = max(dlYPred,[],1);
cmat = aggregateConfusionMetric(cmat,YTest,YPred);
end
evalClipAccuracy = sum(diag(cmat))./sum(cmat,"all")
figure
chart = confusionchart(cmat,classes);

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Accepted Answer

Walter Roberson
Walter Roberson on 19 Sep 2022
Your code has
slowFastClassifier = pretrained.data.Params;
but the actual code (in both the posted example and the mlx file) is
slowFastClassifier = pretrained.data.slowFast;
Because of that, you were trying to run predict() on a struct(), which was getting you a predict() function defined by the System Identification Toolbox instead of the predict() method appropriate for the slowFastVideoClassifier object.

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